Methods and systems for identifying distribution opportunities

ABSTRACT

A computer-implemented method includes receiving, by a first computing device, an identification of a video and an identification of a distribution channel of the video. The method includes retrieving, by the first computing device, from a video sharing network, metadata associated with a video. The method includes retrieving, by the first computing device, from a second computing device in a second network, data having at least one characteristic in common with the metadata. The method includes generating, by the first computing device, a profile of the video based on the retrieved data and the metadata. The method includes generating, by the first computing device, a profile of the distribution channel based on the retrieved data and metadata. The first computing device generates a recommendation for a method to increase a level of distribution of the video. The first computing device provides, to a user, the recommendation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication Ser. No. 61/643,789, filed on May 7, 2012, entitled “Methodsand Systems for Identifying Distribution Opportunities,” which is herebyincorporated by reference.

BACKGROUND

The disclosure relates to data distribution. More particularly, themethods and systems described herein relate to identification ofopportunities to increase a level of distribution of data.

Conventional methods for identifying opportunities to increase a levelof distribution of data typically focus on analyzing an attribute of thedata itself. However, such typical methods may be limited in the abilityto identify additional opportunities for increasing a level ofdistribution of the data since such conventional systems do nottypically provide functionality for analyzing other potentially relevanttypes of data.

BRIEF SUMMARY

In one aspect, a computer-implemented method includes receiving, by afirst computing device, an identification of a video and anidentification of a distribution channel of the video. The methodincludes retrieving, by the first computing device, from a video sharingnetwork, metadata associated with a video. The method includesretrieving, by the first computing device, from a second computingdevice in a second network, data having at least one characteristic incommon with the metadata. The method includes generating, by the firstcomputing device, a profile of the video based on the retrieved data andthe metadata. The method includes generating, by the first computingdevice, a profile of the distribution channel based on the retrieveddata and metadata. The method includes generating, by the firstcomputing device, a recommendation for a method to increase a level ofdistribution of the video. The method includes providing, by the firstcomputing device, to a user, the recommendation.

In another aspect, a system for identifying distribution opportunitiesincludes a data aggregator, a profile generator, and a recommendationengine. The data aggregator executes on a first computing device andreceives an identification of a video and an identification of adistribution channel of the video. The data aggregator retrieves, from avideo sharing network, metadata associated with a video, and retrieves,from a second computing device in a second network, data having at leastone characteristic in common with the metadata. The profile generatorexecutes on the first computing device, generates a profile of the videobased on the retrieved data and the metadata, and generates a profile ofthe distribution channel based on the retrieved data and metadata. Therecommendation engine executes on the first computing device, generatesa recommendation for a method to increase a level of distribution of thevideo, and provides, to a user, the recommendation.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe disclosure will become more apparent and better understood byreferring to the following description taken in conjunction with theaccompanying drawings, in which:

FIGS. 1A-1C are block diagrams depicting embodiments of computers usefulin connection with the methods and systems described herein;

FIG. 2A is a block diagram depicting an embodiment of a distributedsystem for identifying distribution opportunities;

FIG. 2B is a block diagram depicting an embodiment of a system foridentifying distribution opportunities;

FIG. 2C is a screen shot depicting one embodiment of a plurality ofaggregated metrics associated with a particular topic;

FIG. 2D is a screen shot depicting one embodiment of a video profile;

FIG. 2E is a screen shot depicting one embodiment of a distributionchannel profile;

FIG. 2F is a screen shot depicting one embodiment of a display of aplurality of traffic sources used by a particular distribution channel;

FIG. 2G is a screen shot depicting an embodiment displaying avisualization of a plurality of distribution channels sorted byuser-specified criteria;

FIG. 2H is a screen shot depicting one embodiment of a display of videoview distribution by length;

FIG. 2I is a screen shot depicting one embodiment of a recommendationinterface;

FIG. 3 is a flow diagram depicting an embodiment of a method foridentifying distribution opportunities; and

FIG. 4 is a flow diagram depicting an embodiment of a method foridentifying media file distribution opportunities.

DETAILED DESCRIPTION

In some embodiments, the methods and systems described herein providefunctionality for identifying distribution opportunities. Beforedescribing these methods and systems in detail, however, a descriptionis provided of a network in which such methods and systems may beimplemented.

Referring now to FIG. 1A, an embodiment of a network environment isdepicted. In brief overview, the network environment comprises one ormore clients 102 a-102 n (also generally referred to as local machine(s)102, client(s) 102, client node(s) 102, client machine(s) 102, clientcomputer(s) 102, client device(s) 102, computing device(s) 102,endpoint(s) 102, or endpoint node(s) 102) in communication with one ormore remote machines 106 a-106 n (also generally referred to asserver(s) 106 or computing device(s) 106) via one or more networks 104.

Although FIG. 1A shows a network 104 between the clients 102 and theremote machines 106, the clients 102 and the remote machines 106 may beon the same network 104. The network 104 can be a local area network(LAN), such as a company Intranet, a metropolitan area network (MAN), ora wide area network (WAN), such as the Internet or the World Wide Web.In some embodiments, there are multiple networks 104 between the clients102 and the remote machines 106. In one of these embodiments, a network104′ (not shown) may be a private network and a network 104 may be apublic network. In another of these embodiments, a network 104 may be aprivate network and a network 104′ a public network. In still anotherembodiment, networks 104 and 104′ may both be private networks.

The network 104 may be any type and/or form of network and may includeany of the following: a point to point network, a broadcast network, awide area network, a local area network, a telecommunications network, adata communication network, a computer network, an ATM (AsynchronousTransfer Mode) network, a SONET (Synchronous Optical Network) network,an SDH (Synchronous Digital Hierarchy) network, a wireless network and awireline network. In some embodiments, the network 104 may comprise awireless link, such as an infrared channel or satellite band. Thetopology of the network 104 may be a bus, star, or ring networktopology. The network 104 may be of any such network topology as knownto those ordinarily skilled in the art capable of supporting theoperations described herein. The network may comprise mobile telephonenetworks utilizing any protocol or protocols used to communicate amongmobile devices, including AMPS, TDMA, CDMA, GSM, GPRS, or UMTS. In someembodiments, different types of data may be transmitted via differentprotocols. In other embodiments, the same types of data may betransmitted via different protocols.

A client 102 and a remote machine 106 (referred to generally ascomputing devices 100) can be any workstation, desktop computer, laptopor notebook computer, server, portable computer, mobile telephone orother portable telecommunication device, media playing device, a gamingsystem, mobile computing device, or any other type and/or form ofcomputing, telecommunications or media device that is capable ofcommunicating on any type and form of network and that has sufficientprocessor power and memory capacity to perform the operations describedherein. A client 102 may execute, operate or otherwise provide anapplication, which can be any type and/or form of software, program, orexecutable instructions, including, without limitation, any type and/orform of web browser, web-based client, client-server application, anActiveX control, or a Java applet, or any other type and/or form ofexecutable instructions capable of executing on client 102.

In one embodiment, a computing device 106 provides functionality of aweb server. In some embodiments, a web server 106 comprises anopen-source web server such as the APACHE servers maintained by theApache Software Foundation of Delaware. In other embodiments, the webserver executes proprietary software such as the Internet InformationServices products provided by Microsoft Corporation of Redmond, Wash.,the Oracle iPlanet web server products provided by Oracle Corporation ofRedwood Shores, Calif., or the BEA WEBLOGIC products provided by BEASystems of Santa Clara, Calif. In further embodiments, a computingdevice 106 executes self-replication software. In one of theseembodiments, execution of the self-replication software allows acomputing device 106 a to direct a second computing device 106 b toprovide a copy of data stored by the computing device 106. For example,the computing device 106 a may provide access to a web site and, uponexecution of the self-replication software, direct the second computingdevice 106 b to provide access to a copy of the web site.

In some embodiments, the system may include multiple, logically-groupedremote machines 106. In one of these embodiments, the logical group ofremote machines may be referred to as a server farm 38. In another ofthese embodiments, the server farm 38 may be administered as a singleentity.

FIGS. 1B and 1C depict block diagrams of a computing device 100 usefulfor practicing an embodiment of the client 102 or a remote machine 106.As shown in FIGS. 1B and 1C, each computing device 100 includes acentral processing unit 121, and a main memory unit 122. As shown inFIG. 1B, a computing device 100 may include a storage device 128, aninstallation device 116, a network interface 118, an I/O controller 123,display devices 124 a-n, a keyboard 126, a pointing device 127, such asa mouse, and one or more other I/O devices 130 a-n. The storage device128 may include, without limitation, an operating system and software.As shown in FIG. 1C, each computing device 100 may also includeadditional optional elements, such as a memory port 103, a bridge 170,one or more input/output devices 130 a-130 n (generally referred tousing reference numeral 130), and a cache memory 140 in communicationwith the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 122. Inmany embodiments, the central processing unit 121 is provided by amicroprocessor unit such as: those manufactured by Intel Corporation ofMountain View, Calif.; those manufactured by Motorola Corporation ofSchaumburg, Ill.; those manufactured by Transmeta Corporation of SantaClara, Calif.; those manufactured by International Business Machines ofWhite Plains, N.Y.; or those manufactured by Advanced Micro Devices ofSunnyvale, Calif. The computing device 100 may be based on any of theseprocessors, or any other processor capable of operating as describedherein.

Main memory unit 122 may be one or more memory chips capable of storingdata and allowing any storage location to be directly accessed by themicroprocessor 121. The main memory 122 may be based on any availablememory chips capable of operating as described herein. In the embodimentshown in FIG. 1B, the processor 121 communicates with main memory 122via a system bus 150. FIG. 1C depicts an embodiment of a computingdevice 100 in which the processor communicates directly with main memory122 via a memory port 103. FIG. 1C also depicts an embodiment in whichthe main processor 121 communicates directly with cache memory 140 via asecondary bus, sometimes referred to as a backside bus. In otherembodiments, the main processor 121 communicates with cache memory 140using the system bus 150.

In the embodiment shown in FIG. 1B, the processor 121 communicates withvarious I/O devices 130 via a local system bus 150. Various buses may beused to connect the central processing unit 121 to any of the I/Odevices 130, including a VESA VL bus, an ISA bus, an EISA bus, aMicroChannel Architecture (MCA) bus, a PCI bus, a PCI-X bus, aPCI-Express bus, or a NuBus. For embodiments in which the I/O device isa video display 124, the processor 121 may use an Advanced Graphics Port(AGP) to communicate with the display 124. FIG. 1C depicts an embodimentof a computer 100 in which the main processor 121 also communicatesdirectly with an I/O device 130 b via, for example, HYPERTRANSPORT,RAPIDIO, or INFINIBAND communications technology.

A wide variety of I/O devices 130 a-130 n may be present in thecomputing device 100. Input devices include keyboards, mice, trackpads,trackballs, microphones, scanners, cameras, and drawing tablets. Outputdevices include video displays, speakers, inkjet printers, laserprinters, and dye-sublimation printers. An I/O controller 123 as shownin FIG. 1B may control the I/O devices. Furthermore, an I/O device mayalso provide storage and/or an installation device 116 for the computingdevice 100. In some embodiments, the computing device 100 may provideUSB connections (not shown) to receive handheld USB storage devices suchas the USB Flash Drive line of devices manufactured by TwintechIndustry, Inc. of Los Alamitos, Calif.

Referring still to FIG. 1B, the computing device 100 may support anysuitable installation device 116, such as a floppy disk drive forreceiving floppy disks such as 3.5-inch, 5.25-inch disks or ZIP disks, aCD-ROM drive, a CD-R/RW drive, a DVD-ROM drive, tape drives of variousformats, USB device, hard-drive or any other device suitable forinstalling software and programs. The computing device 100 may furthercomprise a storage device, such as one or more hard disk drives orredundant arrays of independent disks, for storing an operating systemand other software.

Furthermore, the computing device 100 may include a network interface118 to interface to the network 104 through a variety of connectionsincluding, but not limited to, standard telephone lines, LAN or WANlinks (e.g., 802.11, T1, T3, 56 kb, X.25, SNA, DECNET), broadbandconnections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet,Ethernet-over-SONET), wireless connections, or some combination of anyor all of the above. Connections can be established using a variety ofcommunication protocols (e.g., TCP/IP, IPX, SPX, NetBIOS, Ethernet,ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), RS232, IEEE802.11, IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, CDMA,GSM, WiMax, and direct asynchronous connections). In one embodiment, thecomputing device 100 communicates with other computing devices 100′ viaany type and/or form of gateway or tunneling protocol such as SecureSocket Layer (SSL) or Transport Layer Security (TLS). The networkinterface 118 may comprise a built-in network adapter, network interfacecard, PCMCIA network card, card bus network adapter, wireless networkadapter, USB network adapter, modem or any other device suitable forinterfacing the computing device 100 to any type of network capable ofcommunication and performing the operations described herein.

In some embodiments, the computing device 100 may comprise or beconnected to multiple display devices 124 a-124 n, each of which may beof the same or different type and/or form. As such, any of the I/Odevices 130 a-130 n and/or the I/O controller 123 may comprise any typeand/or form of suitable hardware, software, or combination of hardwareand software to support, enable or provide for the connection and use ofmultiple display devices 124 a-124 n by the computing device 100. Oneordinarily skilled in the art will recognize and appreciate the variousways and embodiments that a computing device 100 may be configured tohave multiple display devices 124 a-124 n.

In further embodiments, an I/O device 130 may be a bridge between thesystem bus 150 and an external communication bus such as a USB bus, anApple Desktop Bus, an RS-232 serial connection, a SCSI bus, a FireWirebus, a FireWire 800 bus, an Ethernet bus, an AppleTalk bus, a GigabitEthernet bus, an Asynchronous Transfer Mode bus, a HIPPI bus, a SuperHIPPI bus, a SerialPlus bus, a SCI/LAMP bus, a FibreChannel bus, or aSerial Attached small computer system interface bus.

A computing device 100 of the sort depicted in FIGS. 1B and 1C typicallyoperates under the control of operating systems, which controlscheduling of tasks and access to system resources. The computing device100 can be miming any operating system such as any of the versions ofthe MICROSOFT WINDOWS operating systems, the different releases of theUnix and Linux operating systems, any version of the MAC OS forMacintosh computers, any embedded operating system, any real-timeoperating system, any open source operating system, any proprietaryoperating system, any operating systems for mobile computing devices, orany other operating system capable of running on the computing deviceand performing the operations described herein. Typical operatingsystems include, but are not limited to: WINDOWS 3.x, WINDOWS 95,WINDOWS 98, WINDOWS 2000, WINDOWS NT 3.51, WINDOWS NT 4.0, WINDOWS CE,WINDOWS XP, WINDOWS 7, and WINDOWS VISTA, all of which are manufacturedby Microsoft Corporation of Redmond, Wash.; MAC OS, manufactured byApple Inc. of Cupertino, Calif.; OS/2, manufactured by InternationalBusiness Machines of Armonk, N.Y.; and Linux, a freely-availableoperating system distributed by Caldera Corp. of Salt Lake City, Utah,or any type and/or form of a Unix operating system, among others.

The computing device 100 can be any workstation, desktop computer,laptop or notebook computer, server, portable computer, mobile telephoneor other portable telecommunication device, media playing device, agaming system, mobile computing device, or any other type and/or form ofcomputing, telecommunications or media device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein. In someembodiments, the computing device 100 may have different processors,operating systems, and input devices consistent with the device. Inother embodiments the computing device 100 is a mobile device such as aJAVA-enabled cellular telephone or personal digital assistant (PDA). Thecomputing device 100 may be a mobile device such as those manufactured,by way of example and without limitation, by Motorola Corp. ofSchaumburg, Ill.; Kyocera of Kyoto, Japan; Samsung Electronics Co., Ltd.of Seoul, Korea; Nokia of Finland; Hewlett-Packard Development Company,L.P. and/or Palm, Inc. of Sunnyvale, Calif.; Sony Ericsson MobileCommunications AB of Lund, Sweden; or Research In Motion Limited ofWaterloo, Ontario, Canada. In yet other embodiments, the computingdevice 100 is a smart phone, Pocket PC, Pocket PC Phone, or otherportable mobile device supporting Microsoft Windows Mobile Software.

In some embodiments, the computing device 100 is a digital audio player.In one of these embodiments, the computing device 100 is a digital audioplayer such as the Apple IPOD, IPOD Touch, IPOD NANO, and IPOD SHUFFLElines of devices, manufactured by Apple Inc. of Cupertino, Calif. Inanother of these embodiments, the digital audio player may function asboth a portable media player and as a mass storage device. In otherembodiments, the computing device 100 is a digital audio player such asthose manufactured by, for example, and without limitation, SamsungElectronics America of Ridgefield Park, N.J., Motorola Inc. ofSchaumburg, Ill., or Creative Technologies Ltd. of Singapore. In yetother embodiments, the computing device 100 is a portable media playeror digital audio player supporting file formats including, but notlimited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AEFF, Audibleaudiobook, Apple Lossless audio file formats, and .mov, .m4v, and .mp4MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the computing device 100 comprises a combination ofdevices such as a mobile phone combined with a digital audio player orportable media player. In one of these embodiments, the computing device100 is a device in the Motorola line of combination digital audioplayers and mobile phones. In another of these embodiments, thecomputing device 100 is a device in the iPhone smartphone line ofdevices manufactured by Apple Inc. of Cupertino, Calif. In still anotherof these embodiments, the computing device 100 is a device executing theAndroid open source mobile phone platform distributed by the OpenHandset Alliance; for example, the device 100 may be a device such asthose provided by Samsung Electronics of Seoul, Korea or HTCHeadquarters of Taiwan, R.O.C. In other embodiments, the computingdevice 100 is a tablet device such as, for example and withoutlimitation, the iPad line of devices manufactured by Apple Inc.; thePlayBook manufactured by Research in Motion; the Cruz line of devicesmanufactured by Velocity Micro, Inc. of Richmond, Va.; the Folio andThrive line of devices manufactured by Toshiba America InformationSystems, Inc. of Irvine, Calif.; the Galaxy line of devices manufacturedby Samsung; the HP Slate line of devices manufactured byHewlett-Packard; and the Streak line of devices manufactured by Dell,Inc. of Round Rock, Tex.

In some embodiments, the methods and systems described herein providefunctionality for identifying distribution opportunities. In one ofthese embodiments, individuals distributing data may use the methods andsystems described herein to identify additional channels for their datain order to increase a level of distribution of the data. In another ofthese embodiments, and by way of example, a user may be distributingvideo for branding purposes (e.g., to increase a level of awarenessabout themselves, a company, an issue, or other topic) and may identifya first distribution channel for the video; by implementing the methodsand systems described herein, however, the user may identify additionalchannels (e.g., channels not immediately obvious based on an analysis ofthe video) through which he can distribute the video for increasedlevels of distribution.

Referring now to FIG. 2A, a block diagram depicts one embodiment of asystem 200 for identifying distribution opportunities. In briefoverview, the system 200 includes a data aggregator 202, a profilegenerator 204, a recommendation engine 206, a video sharing networkcomputing device 210, a social network computing device 212, a searchengine computing device 214, a client 102, and a machine 106. The client102 and the machine 106 may be provided as machine 100, as described inconnection with FIGS. 1A-1C above.

In one embodiment, the data aggregator 202 executes on the machine 106.In another embodiment, the data aggregator 202 is provided as a softwareapplication. In still another embodiment, the data aggregator 202 isprovided as a hardware application. In another embodiment, the dataaggregator 202 includes a receiver. The receiver may receive data fromthe client 102 such as, without limitation, the identification of thevideo and the identification of the distribution channel of the video,or from other computing devices such as, without limitation, a videosharing network computing device 210, a social network computing device212, or a search engine computing device 214. In still anotherembodiment, the data aggregator 202 includes a transmitter for sendingrequests for data stored on other computing devices. In someembodiments, the data aggregator 202 includes functionality for issuingrequests in accordance with application programming interfaces (API).For example, the data aggregator 202 may include functionality forrequesting data from a social network computing device 212, the requestformatted according to an API made available by the social networkcomputing device 212.

In one embodiment, the profile generator 204 executes on the machine106. In another embodiment, the profile generator 204 is provided as asoftware application. In still another embodiment, the profile generator204 is provided as a hardware application. In another embodiment, theprofile generator 204 includes a receiver for receiving data from othercomponents executing on the machine 106. In still another embodiment,the profile generator 204 includes functionality for storing data in andretrieving data from local or remote databases (not shown). In someembodiments, the profile generator 204 includes functionality forretrieving data from the data aggregator 202 and generating a profile ofa type of data based upon the retrieved data.

In one embodiment, the recommendation engine 206 executes on the machine106. In another embodiment, the recommendation engine 206 is provided asa software application. In still another embodiment, the recommendationengine 206 is provided as a hardware application. In another embodiment,the recommendation engine 206 includes functionality for retrieving aprofile (e.g., from the profile generator 204 or from a database storinggenerated profiles). In still another embodiment, the recommendationengine 206 includes functionality for analyzing retrieved data. In yetanother embodiment, the recommendation engine 206 includes functionalityfor identifying distribution channels and determining whether torecommend the use of the identified distribution channels for increasinga level of distribution of a particular piece of data. For example, andwithout limitation, the recommendation engine 206 may include a profileanalyzer, a channel identifier, a channel analyzer, and a recommendationgenerator (depicted in shadow in FIG. 2A).

In one embodiment, the video sharing network computing device 210 is acomputing device 106 associated with a video sharing network (e.g., aserver owned or maintained or otherwise associated with the videosharing network). Video sharing networks may include, for example andwithout limitation, networks made available by YouTube, LLC of SanBruno, Calif., Vimeo, LLC of New York, N.Y., and Dailymotion, SociétéAnonyme of Paris, France.

In one embodiment, the social networking computing device 212 is acomputing device 106 associated with a social network (e.g., a serverowned or maintained or otherwise associated with the social network).Social networks may include, for example and without limitation,networks made available by Facebook, Inc. of Menlo, Park, Calif.;Twitter, Inc. of San Francisco, Calif.; Linkedln Corporation of MountainView, Calif.; and Pinterest, Inc. of Palo Alto, Calif. In someembodiments, although video sharing networks and social networks mayhave common functionality (e.g., functionality for commenting on datashared by users) and while users of social networks may share data suchas video, the predominant use of a social network need not be videosharing.

In one embodiment, the search engine computing device 214 is a computingdevice 106 associated with a search engine company (e.g., a server ownedor maintained or otherwise associated with a company providing a publicinterface to a search engine). Search engines may include, for exampleand without limitation, the GOOGLE search engine made available byGoogle, Inc. of Mountain View, Calif.; the BING search engine madeavailable by Microsoft Corporation of Redmond, Wash.; the YAHOO! searchengine made available by Yahoo! Inc. of Sunnyvale, Calif.; and theMETACRAWLER search engine made available by InfoSpace, Inc. of Bellevue,Wash. In some embodiments, although video sharing networks and searchengines may have some common functionality (e.g., functionality forsearching for a video shared by a user), the predominant use of aconventional search engine is not typically to share data but rather tofind data. In other embodiments, a video sharing network may provideaccess to search engine functionality. In further embodiments, a socialnetwork may provide access to search engine functionality.

In one embodiment, the client 102 is associated with a user seekingadditional distribution channels for data. For example, the user mayhave created a video and identified a first distribution channel for thevideo. However, by interacting with the components executing on machine106 to implement the methods and systems described herein, the user mayidentify additional distribution channels in which to distribute thevideo. Although depicted in FIG. 2A as a distributed system in which theuser accesses the client 102 to connect to the machine 106, inalternative embodiments, and as shown in FIG. 2B, the user accesses themachine 106 directly.

Referring now to FIG. 3, a flow diagram depicts one embodiment of amethod 300 for identifying distribution opportunities. In briefoverview, the method 300 includes receiving, by a first computingdevice, an identification of a video and an identification of adistribution channel of the video (302). The method 300 includesretrieving, by the first computing device, from a video sharing network,metadata associated with the video (304). The method 300 includesretrieving, by the first computing device, from a second computingdevice in a second network, data having at least one characteristic incommon with the metadata (306). The method 300 includes generating, bythe first computing device, a profile of the video based on theretrieved data and the metadata (308). The method 300 includesgenerating, by the first computing device, a profile of the distributionchannel based on the retrieved data and metadata (310). The method 300includes generating, by the first computing device, a recommendation fora method to increase a level of distribution of the video (312). Themethod 300 includes providing, by the first computing device, to a user,the recommendation (314).

Referring now to FIG. 3 in greater detail, and in connection with FIGS.2A and 2B, the first computing device receives an identification of avideo and an identification of a distribution channel of the video(302). In one embodiment, the machine 106 receives a copy of the video.In another embodiment, the machine 106 receives an identification of acomputing device 106 b hosting the video such as a uniform resourcelocator (URL). In still another embodiment, the identification of thedistribution channel is a URL. In yet another embodiment, thedistribution channel is a channel on the video sharing network; forexample, a themed channel where users upload videos having substantiallysimilar content. In some embodiments, channels are defined by videometadata such as, without limitation, content, themes, topics, keywords,users, actors, producers, the form of technical delivery, andpopularity.

In one embodiment, instead of receiving an identification of a video andan identification of a distribution channel of the video, the machine106 receives metadata for use in identifying distribution opportunities.In such an embodiment, instead of using the identification of the videoand the identification of the distribution channel of the video forretrieving metadata and performing the functionality described below,the machine 106 uses the received metadata to retrieve additionalmetadata and perform the functionality described below.

The first computing device retrieves, from a video sharing network,metadata associated with the video (304). In some embodiments, the dataaggregator 202 retrieves the metadata. In one embodiment, the machine106 analyzes the identified video to identify the metadata to retrieve.For example, in an embodiment in which the machine 106 retrieves a copyof the video, a video analysis component (not shown) analyzes the videofile to identify metadata—such as, without limitation, keywords providedwith the video. In another embodiment, the machine 106 receives themetadata from a user instead of, or in addition to, the identificationof the video and the identification of the distribution channel.However, in other embodiments, the machine 106 does not receive themetadata from the user and instead transmits requests to other computingdevices for the metadata. For example, the machine 106 may use an API toretrieve the metadata from the video sharing network computing device210—for instance, using an API to retrieve comments viewers of the videowrote and uploaded to the distribution channel and analyzing thecomments to identify keywords, sentiment, popularity, and othermetadata.

In some embodiments, the retrieved metadata includes data about adistribution channel that displayed the video. In one of theseembodiments, the retrieved metadata identifies a source from whichtraffic to the distribution channel originated. For example, theretrieved metadata may indicate whether traffic to the distributionchannel originated from advertising displayed on another distributionchannel. As another example, the retrieved metadata may indicate whethertraffic to the distribution channel originated from a viewer clicking ona URL included in a mention of the video shared on a social network. Asa further example, the retrieved metadata may identify a type of devicefrom which a viewer of the video accessed the distribution channel(e.g., from a mobile computing device, from a desktop computing device,or from a tablet computing device). In one embodiment, the machine 106analyzes the retrieved metadata and identifies, based on the analysis, asource from which traffic to the distribution channel originated; themachine 106 may then correlate the identified source with acharacteristic of the distribution channel based upon the identifiedsource and include an identification of the characteristic in thedistribution channel profile 230. In such an embodiment, thechannel-related metadata may provide additional information about thenature of a channel and its relevance to the video. For example,demographic data may be inferred from the channel-related metadata andused in providing a recommendation related to the video. As anotherexample, the machine 106 may correlate one or more relevantcharacteristics, such as the popularity of a channel on a social mediachannel, with one or more types of traffic sources. The machine 106 maydetermine how to perform the correlation based on one or morerequirements specified by a user of the system. For instance, a firstadvertiser might want to target young viewers between 13 and 25 andprefer video channels that have a large footprint on social media whilea second advertiser might want to target men 35-55 who actively searchfor information about a certain type of product; the machine 106 maydetermine which characteristics are relevant and how to correlaterelevant characteristics with a traffic source based on the requirementsof the specific advertiser for which the machine 106 makes arecommendation.

Referring now to FIG. 2C, a screen shot depicts one embodiment of aplurality of aggregated metrics associated with a particular topic. Inone embodiment, the machine 106 aggregates metrics for a plurality ofchannels and a plurality of videos identified by metadata. Theaggregated metrics, such as traffic sources and demographic segments,provide additional information that the machine 106 may access inidentifying additional distribution opportunities for the video.

Referring again to FIG. 3, the method 300 includes retrieving, by thefirst computing device, from a second computing device in a secondnetwork, data having at least one characteristic in common with themetadata (306). In some embodiments, the data aggregator 202 retrievesthe data. In one embodiment, the machine 106 retrieves, from a searchengine computing device 214, search data associated with the metadata.For example, the machine 106 may execute a search (e.g., by executing ascript or using an API) on the search engine for metadata (e.g., for akeyword associated with a video) and determine how the search engineranked the metadata, determine how many other people searched for thesame metadata, or determine how popular the metadata is. In someembodiments, the data aggregator 202 retrieves and aggregates searchdata associated with the distribution channel. In other embodiments, thedata aggregator 202 generates a list of videos and distribution channelsthat have at least one characteristic in common with the metadata based,at least in part, on the retrieved data.

In another embodiment, the first computing device retrieves, from asocial network computing device 212, social network data associated withthe metadata. For example, the machine 106 may retrieve, from a socialnetwork computing device 212, data indicating that a user of the socialnetwork shared the video on the social network and commented on it(e.g., indicated that he liked the video, didn't like the video, or hada comment about the video). In some embodiments, the data aggregator 202retrieves the data. In other embodiments, the data aggregator 202retrieves and aggregates social network data associated with thedistribution channel. In one of these embodiments, the data aggregator202 retrieves and aggregates data associated with both the metadata forthe video and metadata for the distribution channel In otherembodiments, the data aggregator 202 retrieves and aggregates socialnetwork data associated with the video.

In some embodiments, the data aggregator 202 performs an initialanalysis of data retrieved from the second computing device in thesecond network, the data having at least one characteristic in commonwith the metadata. In one of these embodiments, by way of example, thedata aggregator 202 performs an analysis to determine whether toretrieve additional data. For example, the data aggregator may haveretrieved data having at least one characteristic in common with themetadata (such as a similar keyword embedded in a comment, or a synonymof the metadata used as a keyword) and analyze the retrieved data todetermine whether to retrieve additional data (such as, continuing withthe example above, data associated with the synonym of the metadata).Based upon the initial analysis, the data aggregator 202 may determineto retrieve additional data; for instance, retrieving an identificationof a second video associated with a keyword that is a synonym for themetadata. In some embodiments, and as will be discussed in additionaldetail below, the profile generator 204 and the recommendation engine206 may also perform additional analyses of the data.

The first computing device generates a profile of the video based on theretrieved data and the metadata (308). In one embodiment, the profilegenerator 204 executing on the machine 106 generates the profile. Inanother embodiment, the profile generator 204 retrieves data andmetadata from the data aggregator 204. In still another embodiment, theprofile generator 204 retrieves data and metadata from a databasestoring the data and metadata.

Referring to FIG. 2D, a screen shot depicts one embodiment of a videoprofile 220. In the embodiment depicted by FIG. 2D, the video profile220 includes a title, an image selected from the video, a length of thevideo, a number of individuals who have viewed the video, and a numberof times viewers shared the video on one or more social networks.

Referring again to FIG. 3, and in one embodiment, the profile generator204 generates a profile summarizing a level of popularity of the video.In another embodiment, the profile generator 204 generates a profilesummarizing typical sentiments associated with the video (based, forexample, upon how many users said they liked the video in a commenteither in the distribution channel or in a second network). In stillanother embodiment, the profile generator 204 generates a user-friendlyprofile that may be provided to a user (e.g., the user that provided theidentification of the video). In yet another embodiment, the profilegenerator 204 generates a profile that may be used by a softwareapplication for additional analysis (such as, by way of example, therecommendation engine 206). In a further embodiment, the profilegenerator 204 generates a profile including, without limitation, atitle, at least one tag, a description, a length of the video, apublication date, a number of views the video received, popularitymetrics (e.g., number of social sharing operations, number of comments,number of likes, and a number of dislikes), demographic data (e.g., theage groups that watched the video the most), sources of traffic to thevideo, identification of the channel in which the video published, andan identification of an author. In some embodiments, the profilegenerator 204 stores the profile (e.g., in a database accessible to thefirst computing device).

The first computing device generates a profile of the distributionchannel based on the retrieved data and metadata (310). In oneembodiment, the profile generator 204 executing on the machine 106generates the profile. In another embodiment, the profile generator 204retrieves data and metadata from the data aggregator 204. In stillanother embodiment, the profile generator 204 retrieves data andmetadata from a database storing the data and metadata.

Referring now to FIG. 2E, a screen shot depicts one embodiment of adistribution channel profile 230. In the embodiment depicted in FIG. 2E,the distribution channel profile 230 includes a number of videosdistributed in the channel, a number of views of any of the contentdistributed in the distribution channel, a number of distributionchannel subscribers, a number of videos distributed in the distributionchannel that have at least one characteristic in common with themetadata, a number of views of the relevant videos, a number of socialsharing operations on the relevant videos (e.g., a number oflikes/dislikes representing a sentiment level and a number of comments),demographic data (e.g., the age groups that visited the distributionchannel the most), sources of traffic to the distribution channel, auniform resource locator (URL) for the distribution channel, and anyURLs referenced by the distribution channel (such as a URL for apublisher of content to the channel).

In some embodiments, the distribution channel profile 230 includes adescription of at least one characteristic of the distribution channelthat the profile generator 204 identified based upon an analysis of theretrieved data and metadata. In one of these embodiments, for example,the distribution channel profile 230 identifies a level of loyaltybetween a viewer of videos and the distribution channel based on anumber of views by subscribers of distributed videos; the level ofloyalty may be used in additional characteristics of the distributionchannel, such as whether the distribution channel appeals to aparticular demographic, is associated with a particular product orservice brand, or is irrelevant, all of which may inform a decision torecommend one distribution opportunity over another. As another example,the distribution channel profile 230 identifies a level of importance ofsocial sharing to viewers of videos on the distribution channel based ona number of social sharing operations. As another example, thedistribution channel profile 230 identifies a level of engagement ofviewers of videos on the distribution channel based on the number ofsocial sharing operations. The retrieved data and metadata include, asdescribed above (e.g., in connection with FIG. 2D), popularity metrics,social sharing operations, number of views, number of subscribers, thestatistical distribution of the lengths of videos in the distributionchannel, demographic data, sources of traffic to the distributionchannel, and other metrics from which additional channel characteristicsmay be derived.

Referring now to FIG. 2F, a screen shot depicts one embodiment of adisplay of a plurality of traffic sources used by a particulardistribution channel. As depicted in FIG. 2F, distribution channelprofile 230 may include a description of retrieved data and metadata,such as the traffic sources shown in FIG. 2F.

Referring now to FIG. 2G, a screen shot depicts an embodiment displayinga visualization of a plurality of distribution channels sorted byuser-specified criteria. As shown in FIG. 2G, and by way of example,without limitation, the system 200 generates a visualization of the top50 distribution channels sorted by total views for a particular topic.In some embodiments, a size of the squares in the visualizationindicates a number of views of the video. In other embodiments, ashading, pattern, or color of the squares in the visualization indicatesthe sentiment of at least one viewer of the distribution channel withrespect to the distribution channel's content, ranging from mostlynegative to mostly positive.

Referring now to FIG. 2H, a screen shot depicts one embodiment of adisplay of video view distribution by length. As shown in FIG. 2H, themachine 106 may generate analyses of various attributes of videos indistribution channels and use the results of those analyses ingenerating recommendations for additional distribution opportunities. Inthe example shown in FIG. 2H, analysis of the distribution of videolengths for a particular group of channels may be relevant to generationof recommendations because particular users (e.g., advertisers) maychoose to use channels that provide more in-depth (e.g., longer) contentover those that have only superficial content.

Referring again to FIG. 3, and in one embodiment, the profile generator204 generates an enumeration of keywords used by at least one othervideo on the distribution channel, which may be, for example, a secondvideo having at least one characteristic in common with the metadata. Inanother embodiment, the profile generator 204 analyzes the metadata andbuilds a list of n-gram, multi-word expressions that describe themetadata of the distribution channel. In still another embodiment, theprofile generator 204 builds the list of n-gram, multi-word expressionsbased on, without limitation, titles, tags, descriptive texts, and usercomments. In yet another embodiment, the profile generator 204 applies aweight to an entry in the list of n-gram, multi-word expressions,calculated by factors including a frequency of the expression, a numberof different videos that use the expression, and a position of theexpression in the metadata.

The first computing device generates a recommendation for a method toincrease a level of distribution of the video (312). In someembodiments, the recommendation engine 206 generates the recommendation.In one embodiment, the recommendation engine 206 generates therecommendation based at least in part upon an analysis of the profile ofthe video. In another embodiment, the recommendation engine 206generates the recommendation based at least in part upon an analysis ofthe profile of the distribution channel.

In one embodiment, the recommendation engine 206 analyzes the profile ofthe video. In another embodiment, the recommendation engine 206 analyzesthe profile of the distribution channel. In still another embodiment, asub-component of the recommendation engine 206 performs the analyses(e.g., the profile analyzer depicted in shadow in FIGS. 2A and 2B). Insome embodiments, the recommendation engine 206 analyzes newly-generatedprofiles and previously-aggregated data; for example, the recommendationengine 206 analyzes data that the data aggregator 202 aggregated basedon the metadata identified in connection with a request to identifydistribution opportunities for a first video. By comparingnewly-generated profiles with previously-aggregated data, therecommendation engine 206 can determine which channels show aparticularly high degree of activity in the recent past such as a stronggrowth in the number of views, the number of new subscribers, or ofother metrics that might be relevant to determining a recommendation fora particular video. However, in other embodiments, the recommendationengine 206 analyzes previously-generated profiles andpreviously-aggregated data; for example, the recommendation engine 206may determine that data aggregated based on metadata identified inconnection with a first video is relevant to an analysis andidentification of distribution opportunities for a second video and mayanalyze previously-aggregated data.

In one embodiment, the recommendation engine 206 identifies at least onechannel adjacent to the distribution channel based, at least in part, onan analysis of at least one of the profile of the video and the profileof the distribution channel. In some embodiments, the recommendationengine 206 analyzes each piece of data aggregated by the data aggregator202 in order to identify the at least one adjacent channel, iteratingthrough the data. In one of these embodiments, the data aggregator 202has already performed an initial analysis of the aggregated data and therecommendation engine 206 performs a second analysis. In anotherembodiment, the recommendation engine 206 identifies the at least oneadjacent channel by executing a search of a second computing device(e.g., the video sharing network computing device 210, the socialnetwork computing device 212, or the search engine computing device 214)for channels associated with a characteristic identified in the profileof the video or associated with a characteristic identified in theprofile of the distribution channel. In still another embodiment, asub-component of the recommendation engine 206 identifies the at leastone adjacent channel (e.g., the channel identifier depicted in shadow inFIGS. 2A and 2B).

In some embodiments, the recommendation engine 206 identifies the atleast one adjacent channel by analyzing a level of relatedness betweenthe at least one adjacent channel and the distribution channel. In oneof these embodiments, the recommendation engine 206 determines that ahigh degree of overlap exists between metadata associated with the twochannels (e.g., the keywords, tags, demographic profile, or othermetadata associated with the two channels). In another of theseembodiments, the recommendation engine 206 determines that the at leastone adjacent channel is related to the distribution channel based uponan explicitly defined relation, such as a subscription relationshipbetween an owner of the distribution channel and the adjacent channel,or vice versa.

In some embodiments, the recommendation engine 206 identifies the atleast one adjacent channel indirectly. In one of these embodiments, therecommendation engine 206 identifies a second video based on theanalyses of the profile of the video and of the profile of thedistribution channel. In another of these embodiments, therecommendation engine 206 identifies a second distribution channelassociated with the second video.

In one embodiment, the recommendation engine 206 determines that the atleast one channel adjacent to the distribution channel has at least onecharacteristic in common with the distribution channel. For example, theadjacent channel may have a common topic, theme, classification, orother characteristic in common with the distribution channel. In anotherembodiment, the recommendation engine 206 transmits an identification ofthe adjacent channel to the profile generator 204, which generates aprofile of the adjacent channel; the recommendation engine 206 analyzesthe generated profile of the adjacent channel to determine whether theadjacent channel has at least one characteristic in common with thedistribution channel. In some embodiments, a sub-component of therecommendation engine 206 performs the analysis of the adjacent channelto determine whether the adjacent channel has at least onecharacteristic in common with the distribution channel (e.g., thechannel analyzer depicted in shadow in FIGS. 2A and 2B). In otherembodiments, to determine that the at least one adjacent channel has atleast one characteristic in common with the distribution channel, therecommendation engine 206 retrieves a score for the at least oneadjacent channel. In one of these embodiments, the recommendation engine206 analyzes a score based on a plurality of weighted factors. Factorsmay include, by way of example, and without limitation, a number ofviews the channel has, a number of subscribers, a level of usersentiment for the channel, a level of social media popularity of thechannel (e.g., a number of sharing operations), the demographic profileof the channel, the sources of traffic to the channel, the distributionof length of the videos on the channel, and a degree of overlap betweenthe original distribution channel and the at least one adjacent channelbased on a weighted list of n-gram, multi-word expressions.

In one embodiment, the recommendation engine 206 determines thatdistributing the video on the adjacent channel could enhance a level ofdistribution of the video. In another embodiment, the recommendationengine 206 makes the determination based upon, without limitation, ananalysis of the score for the adjacent channel as described above. Instill another embodiment, and by way of example, the recommendationengine 206 may determine that the distribution channel and the adjacentchannel are two separate channels but attract similar users, havesimilar themes, distribute videos or other data of similar content, orhave another characteristic in common that would improve a level ofdistribution of the video. In another embodiment, the recommendationengine 206 determines that distributing the video on the adjacentchannel would increase a number of viewers of the video. In stillanother embodiment, the recommendation engine 206 calculates a predictedincrease in the number of viewers of the video based on previous viewernumbers of the adjacent channel and viewer numbers of comparable videos.In still another embodiment, the recommendation engine 206 determinesthat distributing the video on the adjacent channel would increase anamount of traffic to a web site associated with the video. In yetanother embodiment, a sub-component of the recommendation engine 206makes the determination and generates the recommendation based upon thedetermination (e.g., the recommendation generator depicted in shadow inFIGS. 2A and 2B).

In one embodiment, the recommendation engine 206 may determine thatplacing an advertisement on the adjacent channel could enhance a levelof distribution of the video. In another embodiment, the recommendationengine 206 may determine that promotion on a freely accessible socialchannel could enhance a level of distribution of the video. In stillanother embodiment, the recommendation engine 206 may determine thatother forms of activity such as, without limitation, social interaction(e.g., commenting) and commercial distribution deals, could enhance alevel of distribution of the video. In yet another embodiment, therecommendation engine 206 may identify an indirect form of activity thatcould enhance a level of distribution of the video such as, withoutlimitation, video search engine optimization (SEO) in which revisingkeyword strategies to match strategies of the adjacent channel mayenhance the level of distribution. In some embodiments, therecommendation engine 206 generates content and marketing ideas from therecommendation, going beyond simply identifying distributionopportunities. In one of these embodiments, by way of example, therecommendation engine 206 may determine, based at least in part on dataidentified by the system, that people interested in a particular productseem to like a particular type of content (e.g., without limitation,tutorial videos), and may determine to recommend the production ofsimilar types of content. In yet another embodiment, the recommendationengine 206 may determine, based at least in part on data identified bythe system, that people interested in a particular topic prefer videosof a certain length (e.g., without limitation, shorter clips of oneminute or less, or longer educational content of 10 minutes of more) andmay recommend the production of videos of a similar length. In yetanother embodiment, the recommendation engine 206 may determine, basedat least in part on data identified by the system, that people in acertain demographic group are interested in particular types of content.In some embodiments, the recommendation engine 206 may combine severalof the identified factors, such as topics people are interested in, thedemographic group they belong to, or products they are interested in, todetermine what type of content these people prefer, or that these peopleprefer videos of a certain length.

The first computing device provides, to a user, the recommendation(314). In one embodiment, the machine 106 transmits the recommendationto the client 102 associated with the user (as shown in FIG. 2B). Inanother embodiment, the machine 106 generates a display of therecommendation for a local user.

Referring now to FIG. 2I, a screen shot depicts one embodiment of arecommendation interface 240. In the embodiment depicted in FIG. 2I, therecommendation interface 240 includes an identification of at least someof the metadata (e.g., “Keywords: Video marketing, video SEO”), anidentification of advertising placement recommendations, and anidentification of additional distribution channels in which todistribute the video, as well as interface options for requestingadditional information (e.g., “Show more,” “Create SEO keyword list,”and “Create targeting plan”).

In some embodiments, the recommendation interface 240 provides not justa recommendation but additional information underlying the decision torecommend a particular distribution opportunity. For example, therecommendation may include some or all of the information stored in adistribution channel profile 230 including, for example, a descriptionof at least one characteristic of the distribution channel that theprofile generator 204 inferred from retrieved data and metadata (such asbrand loyalty, engagement, and influence).

Although described herein in connection with identifying distributionopportunities for videos, those of ordinary skill in the art willrecognize that the methods and systems described herein may be used inconnection with identifying distribution opportunities for other typesof data. For example, the methods and systems described herein may beimplemented to identify distribution opportunities for media objectsgenerally. As one example, the methods and systems described herein mayidentify distribution opportunities for photographs, music, and othermedia objects.

Referring now to FIG. 4, a flow diagram depicts one embodiment of amethod 400 for identifying distribution opportunities. In briefoverview, the method 400 includes receiving, by a first computingdevice, an identification of a media file and an identification of adistribution channel of the media file (402). The method 400 includesretrieving, by the first computing device, from a media file sharingnetwork, metadata associated with the media file (404). The method 400includes retrieving, by the first computing device, from a secondcomputing device in a second network, data having at least onecharacteristic in common with the metadata (406). The method 400includes generating, by the first computing device, a profile of themedia file based on the retrieved data and the metadata (408). Themethod 400 includes generating, by the first computing device, a profileof the distribution channel based on the retrieved data and metadata(410). The method 400 includes generating, by the first computingdevice, a recommendation for a method to increase a level ofdistribution of the media file (412). The method 400 includes providing,by the first computing device, to a user, the recommendation (414).

A first computing device receives an identification of a media file andan identification of a distribution channel of the media file (402). Asdescribed above in connection with FIG. 3, the first computing devicemay receive an identification of a video and an identification of adistribution channel of the video. However, those of ordinary skill inthe art understand that the method is not limited to a specific type ofmedia file. In some embodiments, for example, the first computing devicereceives an identification of a digital photograph and an identificationof a distribution channel of the digital photograph. Distributionchannels for digital photographs may include, by way of example andwithout limitation, the Instagram web site maintained by Instagram, LLCof Menlo Park, Calif.; the Flickr web site maintained by Yahoo! Inc. ofSunnyvale, Calif.; and the Pinterest web site maintained by Pinterest,Inc. of Palo Alto, Calif. In other embodiments, the first computingdevice receives an identification of a digital music file and anidentification of a distribution channel of the digital music file.Distribution channels for digital music files may include, by way ofexample and without limitation, the SoundCloud web site maintained bySoundCloud Limited of Berlin, Germany, and the MySpace web sitemaintained by MySpace, LLC of Beverly Hills, Calif.

The first computing device retrieves, from a media file sharing network,metadata associated with the media file (404). The first computingdevice may retrieve the metadata from the media file sharing network asdescribed above in connection with FIG. 3. The first computing devicemay identify a media file sharing network from which to retrieve themetadata based on a type of the media file. For example, the machine 106may identify a photo sharing site when the media file is a digitalphotograph, while identifying a video sharing site when the media fileis a video. Those of ordinary skill in the art will understand that themachine 106 may analyze media files of different types in substantiallysimilar ways, allowing the machine 106 to determine which media filesharing network to access in order to retrieve the metadata regardlessof what type of media file is at issue.

The first computing device retrieves, from a second computing device ina second network, data having at least one characteristic in common withthe metadata (406). The first computing device may retrieve the datafrom the second network as described above in connection with FIG. 3.

The first computing device generates a profile of the media file basedon the retrieved data and the metadata (408). The first computing devicemay generate the profile as described above in connection with FIG. 3.

The first computing device generates a profile of the distributionchannel based on the retrieved data and the metadata (410). The firstcomputing device may generate the profile as described above inconnection with FIG. 3.

The first computing device generates a recommendation for a method toincrease a level of distribution of the media file (412). The firstcomputing device may generate the recommendation as described above inconnection with FIG. 3.

The first computing device provides, to a user, the recommendation(414). The first computing device may provide the recommendation asdescribed above in connection with FIG. 3.

It should be understood that the systems described above may providemultiple ones of any or each of the described components and that thesecomponents may be provided on either a standalone machine or, in someembodiments, on multiple machines in a distributed system. It shouldalso be understood that phrases such as “based on” and “based upon” donot imply “based exclusively on” and instead generally mean that theparticular feature, structure, step, or characteristic is based at leastin part on the specified element. Further, the phrases ‘in oneembodiment,’ ‘in another embodiment,’ and the like, generally mean thatthe particular feature, structure, step, or characteristic following thephrase is included in at least one embodiment of the present disclosureand may be included in more than one embodiment of the presentdisclosure. However, such phrases do not necessarily refer to the sameembodiment.

The systems and methods described above may be implemented as a method,apparatus, or article of manufacture using programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof. The techniques described above may be implementedin one or more computer programs executing on a programmable computerincluding a processor, a storage medium readable by the processor(including, for example, volatile and non-volatile memory and/or storageelements), at least one input device, and at least one output device.Program code may be applied to input entered using the input device toperform the functions described and to generate output. The output maybe provided to one or more output devices.

Each computer program within the scope of the claims below may beimplemented in any programming language, such as assembly language,machine language, a high-level procedural programming language, or anobject-oriented programming language. The programming language may, forexample, be LISP, PROLOG, PERL, Python, C, C++, C#, JAVA, or anycompiled or interpreted programming language.

Each such computer program may be implemented in a computer programproduct tangibly embodied in a machine-readable storage device forexecution by a computer processor. Method steps of the invention may beperformed by a computer processor executing a program tangibly embodiedon a computer-readable medium to perform functions of the invention byoperating on input and generating output. Suitable processors include,by way of example, both general and special purpose microprocessors.Generally, the processor receives instructions and data from a read-onlymemory and/or a random access memory. Storage devices suitable fortangibly embodying computer program instructions include, for example,all forms of computer-readable devices, firmware, programmable logic,hardware (e.g., integrated circuit chip; electronic devices; acomputer-readable non-volatile storage unit; non-volatile memory, suchas semiconductor memory devices, including EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROMs). Any of the foregoing may besupplemented by, or incorporated in, specially-designed ASICs(application-specific integrated circuits) or FPGAs (Field-ProgrammableGate Arrays). A computer can generally also receive programs and datafrom a storage medium such as an internal disk (not shown) or aremovable disk. These elements will also be found in a conventionaldesktop or workstation computer as well as other computers suitable forexecuting computer programs implementing the methods described herein,which may be used in conjunction with any digital print engine ormarking engine, display monitor, or other raster output device capableof producing color or gray scale pixels on paper, film, display screen,or other output medium. A computer may also receive programs and datafrom a second computer providing access to the programs via a networktransmission line, wireless transmission media, signals propagatingthrough space, radio waves, infrared signals, etc.

Having described certain embodiments of methods and systems foridentifying distribution opportunities, it will now become apparent toone of skill in the art that other embodiments incorporating theconcepts of the disclosure may be used. Therefore, the disclosure shouldnot be limited to certain embodiments, but rather should be limited onlyby the spirit and scope of the following claims.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a first computing device, an identification of a video andan identification of a distribution channel of the video; retrieving, bythe first computing device, from a video sharing network, metadataassociated with the video; retrieving, by the first computing device,from a second computing device in a second network, data having at leastone characteristic in common with the metadata; generating, by the firstcomputing device, a profile of the video based on the retrieved data andthe metadata; generating, by the first computing device, a profile ofthe distribution channel based on the retrieved data and the metadata;generating, by the first computing device, a recommendation for a methodto increase a level of distribution of the video; and providing, by thefirst computing device, to a user, the recommendation.
 2. The method ofclaim 1, wherein retrieving from the second computing device furthercomprises retrieving, by the first computing device, from a searchengine computing device, search data associated with the metadata. 3.The method of claim 1, wherein retrieving from the second computingdevice further comprises retrieving, by the first computing device, fromat least one social network, social network data associated with themetadata.
 4. The method of claim 1, wherein generating the profile ofthe distribution channel further comprises: identifying, by the firstcomputing device, a characteristic of the distribution channel basedupon the retrieved metadata and the retrieved data having at least onecharacteristic in common with the metadata; and including, by the firstcomputing device, an identification of the characteristic in the profileof the distribution channel.
 5. The method of claim 1, whereingenerating the recommendation further comprises: analyzing the profileof the video; analyzing the profile of the distribution channel;identifying, by the first computing device, at least one channeladjacent to the distribution channel based on the analyses; andgenerating a recommendation to distribute the video in the at least onechannel adjacent to the distribution channel based on the analyses. 6.The method of claim 1, wherein generating the recommendation furthercomprises: analyzing the profile of the video; analyzing the profile ofthe distribution channel; identifying, by the first computing device, asecond video based on the analyses; identifying, by the first computingdevice, a second distribution channel associated with the second video;and generating a recommendation to distribute the video in the seconddistribution channel.
 7. The method of claim 1 further comprising:analyzing, by the first computing device, the metadata retrieved fromthe video sharing network; and identifying, by the first computingdevice, based on the analysis, a source from which traffic to thedistribution channel originated.
 8. The method of claim 7, whereingenerating the profile of the distribution channel further comprises:correlating, by the first computing device, the identified source with acharacteristic of the distribution channel based upon the identifiedsource; and including, by the first computing device, an identificationof the characteristic in the profile of the distribution channel.
 9. Anon-transitory, computer readable medium comprising computer programinstructions tangibly stored on the computer readable medium, whereinthe computer program instructions are executable by at least onecomputer processor to perform a method, the method comprising:receiving, by a first computing device, an identification of a video andan identification of a distribution channel of the video; retrieving, bythe first computing device, from a video sharing network, metadataassociated with a video; retrieving, by the first computing device, froma second computing device in a second network, data having at least onecharacteristic in common with the metadata; generating, by the firstcomputing device, a profile of the video based on the retrieved data andthe metadata; generating, by the first computing device, a profile ofthe distribution channel based on the retrieved data and metadata;generating, by the first computing device, a recommendation for a methodto increase a level of distribution of the video; and providing, by thefirst computing device, to a user, the recommendation.
 10. A systemcomprising: a data aggregator (i) executing on a first computing device,(ii) receiving an identification of a video and an identification of adistribution channel of the video, (iii) retrieving, from a videosharing network, metadata associated with a video, and (iv) retrieving,from a second computing device in a second network, data having at leastone characteristic in common with the metadata; a profile generator (i)executing on the first computing device, (ii) generating a profile ofthe video based on the retrieved data and the metadata, and (iii)generating a profile of the distribution channel based on the retrieveddata and metadata; and a recommendation engine (i) executing on thefirst computing device, (ii) generating a recommendation for a method toincrease a level of distribution of the video, and (iii) providing, to auser, the recommendation.